Air conditioning control system

ABSTRACT

An air conditioning control system for controlling an air conditioner specifies a condition in an environment, detects a state amount representing the environment, infers a control method for the air conditioner in the environment based on the state amount that is detected, and controls the air conditioner based on the control method that is inferred. Further, the air conditioning control system generates or updates a plurality of learning models through machine learning using the state amount that is detected and stores the plurality of learning models in a manner such that they are associated with combinations of conditions in the environment.

BACKGROUND OF THE INVENTION 1. Field of the Invention

The present invention relates to an air conditioning control system andespecially relates to an air conditioning control system that controlsair conditioning while switching learning models in accordance with aninstallation state and an operation state of machines in a factory.

2. Description of the Related Art

Since a plurality of machines (machine tools, for example) ate installedin a factory and the temperature in the factory affects machiningaccuracy in precision machining, an air conditioner for controllingenvironment conditions inside the factory is provided. Production in afactory is instructed based on a production plan. In order to produceproducts instructed in the production plan by an instructed deliverydate, operation states and machining conditions of a plurality ofmachines arc controlled find an operation state of an air conditioner iscontrolled to realize temperature conditions (temperature anduniformity) in the factory which are required for machining.

As a technique for controlling air conditioning so as to satisfytemperature conditions in a factory which are required for machining,Japanese Patent Application Laid-Open No. 06-307703 and Japanese PatentApplication Laid-Open No. 2015-2063519, for example, disclose atechnique in which a plurality of air conditioners are controlled in alump so as to maintain a temperature in a factory at a predeterminedtemperature.

However, temperature distribution in a factory varies depending oninstallation states of machines (machine tools, robots, and the like) asheat generating sources installed in the factory and operation states ofrespective machines. Therefore, in order to realize temperatureconditions in a factory required for machining, it is necessary tocontrol air conditioners while taking into account installation statesand operation states of respective machines. It is, however, difficultto appropriately control air conditioning in accordance with suchvarious situations.

It is conceivable to introduce a machine learning device so as tocontrol air conditioning. However, much state information detected invarious situations are required and many parameters including datarelated to situations are required so as to produce a versatile machinelearning device (versatile learning model) capable of coping withvarious situations described above. Accordingly, known problems such asover learning may occur.

SUMMARY OF THE INVENTION

An object of the present invention is to provide an air conditioningcontrol system that is capable of performing appropriate airconditioning control, in which installation states and operation statesof machines are taken into account, in a wider range.

An air conditioning control system according to the present invention isprovided with a mechanism that switches learning models, which are usedfor determining an air conditioning control action to be used, dependingon installation states and operation states of machines in a factory,thereby solving the above-mentioned problems. The air conditioningcontrol system according to the present invention has a plurality oflearning models, selects a learning model depending on installationsuites and operation states, for example, of machines in a factory,performs machine learning with respect to the selected learning modelbased on a state amount detected from the factory, and properly useslearning models thus created, depending on the installation states andthe operation states, for example, of machines in the factory so as tocontrol an air conditioner.

An air conditioning control system according to an aspect of the presentinvention controls an air conditioner in an environment in which atleast one machine is installed. The air conditioning control systemincludes: a condition specification unit that specifies a condition inthe environment; a state amount detection unit that detects a stateamount representing a state of the environment; an inference calculationunit that infers a control method for the air conditioner in theenvironment based on the state amount; an air conditioning control unitthat controls the air conditioner based on the control method that isinferred by the inference calculation unit; a learning model generationunit that generates or updates a learning model through machine learningusing the state amount; and a learning model storage unit that storesone or more learning models generated by the learning model generationunit in a manner such that the one or more learning model are associatedwith a combination of conditions specified by the conditionspecification unit. The inference calculation unit calculates a controlmethod for the air conditioner in the environment managed by the airconditioning control system, by selectively using one or more learningmodels among learning models stored in the learning model storage unit,based on the condition in the environment that is specified by thecondition specification unit.

The air conditioning control system may further include a feature amountcreation unit that creates a feature amount characterizing theenvironment based on the state amount detected by the state amountdetection unit, wherein the inference calculation unit may infer acontrol method for the air conditioner in the environment based on thefeature amount, and the learning model generation unit may generate orupdate a learning model through machine learning using the featureamount.

The learning model generation unit may alter an existing learning modelstored in the learning model storage unit so as to generate a newlearning model.

The learning model storage unit may encrypt and store a learning modelgenerated by the learning model generation unit, and decrypt theencrypted learning model when the learning model encrypted is read bythe inference calculation unit.

An air conditioning control system according to another aspect of thepresent invention controls an air conditioner in an environment in whichone or more machines are installed. The air conditioning control systemincludes: a condition specification unit that specifies a condition inthe environment; a state amount detection unit that detects a stateamount representing the environment:

an inference calculation unit that infers a control method for the airconditioner in the environment based on the state amount; an airconditioning control unit that controls the air conditioner based on thecontrol method that is inferred by the inference calculation unit; and alearning model storage unit that stores at least one learning model thatis preliminarily associated with a combination of conditions in theenvironment. The inference calculation unit calculates a control methodfor the air conditioner in the environment by selectively using one ormore learning models among the learning models stored in the learningmodel storage unit, based on the condition in the environment that isspecified by the condition specification unit.

The air conditioning control system may further includes a featureamount creation unit that creates a feature amount characterizing theenvironment based on the state amount, wherein the inference calculationunit may infers, based on the feature amount, a control method for theair conditioner in the environment managed by the air conditioningcontrol system.

An air conditioning controller according to still another aspect of thepresent invention includes the condition specification unit and thestate amount detection unit which are described above.

An air conditioning control method according to yet another aspect ofthe present invention includes: a step for specifying a condition forcontrolling an air conditioner in an environment in which one or moremachines are installed; a step for detecting a state amount representingthe environment; a step for inferring a control method for the airconditioner in the environment based on the state amount; a step forcontrolling the air conditioner based on the control method; and a stepfor generating or updating a learning model through machine learningusing the state amount. In the step for inferring, a learning model tobe used based on the condition in the environment that is specified inthe step for specifying is selected from the one or more learning modelsthat are preliminarily associated with a combination of conditions inthe environment, and a control method for the air conditioner in theenvironment is calculated using the learning model that is selected.

The air conditioning control method may further include a step forcreating a feature amount characterizing the environment based on thestate amount, wherein in the step for inferring, a control method forthe air conditioner in the environment may be inferred based on thefeature amount, and in the step for generating or updating a learningmodel, a learning model may be generated or updated through machinelearning using the feature amount.

An air conditioning control method according to yet another aspect ofthe present invention includes: a step for specifying a condition forcontrolling an air conditioner in an environment in which one or moremachines are installed; a step for detecting a state amount representingthe environment; a step for inferring a control method for the airconditioner in the environment based on the state amount; and a step forcontrolling the air conditioner based on the control method. In the stepfor inferring, a learning model to be used based on the condition in theenvironment that is specified in the step for specifying is selectedfrom one or more learning models that are preliminarily associated witha combination of conditions in the environment and a control method forthe air conditioner in the environment is calculated using the learningmodel that is selected.

The air conditioning control method may further includes a step forcreating a feature amount characterizing the environment based on thestate amount, wherein in the step for inferring, a control method forthe air conditioner in the environment may be inferred based on thefeature amount.

A learning model set according to yet another aspect of the presentinvention includes a plurality of learning models each of which isassociated with a combination of conditions for controlling an airconditioner in an environment in which one or more machines areinstalled. Each of the plurality of learning models is a learning modelgenerated or updated, in a condition in the environment, based on astate amount, representing the environment, and one learning model isselected from the plurality of learning models based on a condition setin an environment, and the learning model that is selected is used forprocessing of inferring a control method for the air conditioner in theenvironment.

According to the present invention, since machine learning can beperformed, based on a state amount detected in each state, with respectto a learning model selected depending on installation states andoperation states of machines in a factory, machine learning can beefficiently performed while preventing over learning. Further, since anair conditioner is controlled by using a learning model selecteddepending on installation states and operation states, for example, ofmachines in a factory, accuracy in control of the air conditioner isimproved.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a functional block diagram schematically illustrating an airconditioning control system according to a first embodiment.

FIG. 2 is a drawing showing an example of a model in an environmentmanaged by the air conditioning control system.

FIG. 3 is a functional block diagram schematically illustrating on airconditioning control system according to a second embodiment.

FIG. 4 is a functional block diagram schematically illustrating an airconditioning control system according to a third embodiment.

FIG. 5 is a functional block diagram schematically illustrating an airconditioning control system according to a fourth embodiment.

FIG. 6 is a functional block diagram schematically illustrating an airconditioning control system according to a fifth embodiment.

FIG. 7 is a functional block diagram schematically illustrating amodification of the air conditioning control system according to thefifth embodiment.

FIG. 8 is a functional block diagram schematically illustrating anair-conditioning control system according to a sixth embodiment.

FIG. 9 is a schematic flowchart of processing executed in the airconditioning control system illustrated in any of FIGS. 6 to 8.

FIG. 10 is a schematic flowchart of processing executed in the airconditioning control system illustrated in any of FIG. 1 and FIGS. 3 to5.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

FIG. 1 is a functional block diagram schematically illustrating an airconditioning control system 1 according to a first embodiment.

Each of functional blocks illustrated in FIG. 1 is implemented such thata processor such as a CPU and a GPU included in a computer such as anumerical controller, a cell computer, a host computer, and a cloudserver controls an operation of each unit of a device in accordance witheach system program.

An air conditioning control system 1 according to the present embodimentincludes an environment management unit 100, an inference processingunit 200, and a learning model storage unit 300. The environmentmanagement unit 100 manages an environment (a room in which an airconditioner 130 is installed, for example) at least a state of which isan object of observation and inference. The inference processing unit200 infers a state of the environment. The learning model storage unit300 stores and manages a plurality of learning models. This airconditioning control system 1 further includes an air conditioningcontrol unit 400 and a learning model generation unit 500. The airconditioning control unit 400 controls air conditioning based on aresult obtained through inference of a state of an environment performedby the inference processing unit 200. The learning model generation unit500 generates and updates learning models to be stored in the learningmodel storage unit 300.

The environment management unit 100 according to the present embodimentspecifics conditions for controlling the air conditioner 130 in anenvironment in which the air conditioner 130 controlled by the airconditioning control system 1 is installed (an environment managed bythe air conditioning control system 1) and acquires a state amountrepresenting a state of the environment. This environment managementunit 100 can be mounted on a central management device of airconditioners or a numerical controller installed in an environmentmanaged by the air conditioning control system 1, for example. Theenvironment management unit 100 is configured to be connected with awired/wireless network 150 so as to be able to exchange data withmachines 120, which are installed in an environment managed by the airconditioning control system 1, and the air conditioners 130 controlledby the air conditioning control system 1.

A condition specification unit 110 included in the environmentmanagement unit 100 specifies conditions (installation states of themachines 120 and the air conditioners 130 and operation states of themachines 120, for example) in an environment managed by the airconditioning control system 1.

The installation states of the machines 120 and the air conditioners 130are represented as how the machines 120 as heat sources are installed ineach of a plurality of areas which are obtained by dividing onenvironment managed by the air conditioning control system 1, asillustrated in FIG. 2, for example. As the installation states of themachines 120 and the air conditioners 130 in the example illustrated inFIG. 2, the machines 120 as heat sources are installed in areas 2, 7,11, and 12 and the air conditioner 130 is installed in area 5, forexample. The environment management unit 100 may acquire suchinstallation states of the machines 120 and the air conditioners 130through setting by an operator, for example, via an input/output devicewhich is not illustrated.

Operation states of the machines 120 are acquired as operation states ofrespective machines 120 operating in an environment managed by the airconditioning control system 1. Operation states of the machines 120 maybe represented in classification of calorific values corresponding tooperations of the machines 120, such as stop, low operation (lowtemperature), medium operation (medium temperature), and high operation(high temperature), for example. The environment management unit 100 mayset operation states of the machines 120 based on operation states ofthe machines 120 (an internal temperature of the machine, motions of amain spindle, a feed axis, and the like, and a load state, for example)acquired from respective machines 120 via the network 150.

The condition specification unit 110 specifies (outputs) conditions inan environment managed by the air conditioning control system 1, whichare acquired as described above, with respect to the learning modelstorage unit 300 and the learning model generation unit 500. Thecondition specification unit 110 plays a role in notifying each unit ofthe air conditioning control system 1 of conditions of current airconditioning control of the environment management unit 100 asconditions for selecting a learning model.

A state amount detection unit 140 included in the environment managementunit 100 detects a state of an environment managed by the airconditioning control system 1 as a state amount. Examples of the stateamount of an environment managed by the air conditioning control system1 include an ambient temperature, an internal temperature, and anoperation state of each machine 120, and a temperature of each area inthe environment managed by the air conditioning control system 1. Thestate amount detection unit 140 detects detection values which aredetected by temperature sensors provided to the machines 120 and the airconditioners 130 and a temperature sensor installed in each area in anenvironment managed by the air conditioning control system 1, as stateamounts. The state amounts detected by the state amount detection unit140 are outputted to the inference processing unit 200 and the learningmodel generation unit 500.

The inference processing unit 200 according to the present embodimentobserves a state, which is acquired from the environment management unit100, of an environment managed by the air conditioning control system 1and infers the environment managed by the air conditioning controlsystem 1 based on this observation result. The inference processing unit200 can be mounted on an air conditioning controller, a cell computer, ahost computer, a cloud server, a machine learning device, or the like,for example.

A feature amount creation unit 210 included in the inference processingunit 200 creates a feature amount representing a feature of anenvironment managed by the air conditioning control system 1 based onstate amounts detected by the state amount detection unit 140. Thefeature amount which is created by the feature amount creation unit 210and represents a feature of an environment managed by the airconditioning control system 1 is useful information as a determinationmaterial of air conditioning control by the air conditioning controlunit 400. Further, the feature amount which is created by the featureamount creation unit 210 and represents a feature of an environmentmanaged by the air conditioning control system 1 is input data to beused when an inference calculation unit 220, which will be describedlater, performs inference using a learning model. The feature amountwhich is created by the feature amount creation unit 210 and representsa feature of an environment managed by the air conditioning controlsystem 1 may be an ambient temperature obtained by sampling ambienttemperatures, detected by the state amount detection unit 140, of eachmachine 120 in predetermined sampling cycles for the past predeterminedperiod, a peak value in operation states, detected by the state amountdetection unit 140, of each machine 120 in the past predeterminedperiod, or a combination of signal processing with respect totemperatures, detected by the state amount detection unit 140, ofrespective areas such as integration and conversion into a time-seriesfrequency domain, standardization of an amplitude or power density,adaptation to a transfer function, dimensional reduction to specifictime or specific frequency width, for example. The feature amountcreation unit 210 performs preprocessing of state amounts detected bythe state amount detection unit 140 and normalizes the state amounts sothat the inference calculation unit 220 can deal with the state amounts.

The inference calculation unit 220 included in the inference processingunit 200 infers a method for controlling the air conditioner 130 in anenvironment managed by the air conditioning control system 1 based on alearning model, which is selected from the learning model storage unit300 based on conditions in a current environment managed by the airconditioning control system 1, and based on a feature amount, which iscreated by the feature amount creation unit 210. The inferencecalculation unit 220 is implemented by applying a learning model storedin the learning model storage unit 300 to a platform in which inferenceprocessing based on machine learning can be executed. The inferencecalculation unit 220 may be used for performing inference processingusing a multilayer neural network, or used for performing inferenceprocessing using a known learning algorithm as machine learning forBayesian network, a support vector machine, mixture Gaussian model, andthe like, for example. The inference calculation unit 220 may be usedfor performing inference processing using a learning algorithm forsupervised learning, unsupervised learning, reinforcement learning, andthe like, for example. Further, the inference calculation unit 220 maybe capable of executing inference processing respectively based on aplurality of kinds of learning algorithms. The inference calculationunit 220 constitutes a machine learning device based on a learningmodel, which is selected from the learning model storage unit 300, ofmachine learning, and executes inference processing using a featureamount created by the feature amount creation unit 210 as input data ofthis machine learning device so as to infer a method for controlling theair conditioner 130 in an environment managed by the air conditioningcontrol system 1. The method for controlling the air conditioner 130which is a result of the inference by the inference calculation unit 220may be a set temperature or a wind direction (including a swing action,for example) of each air conditioner 130 installed in an environmentmanaged by the air conditioning control system 1, for example.

The learning model storage unit 300 according to the present embodimentis capable of storing a plurality of learning models which areassociated with combinations of conditions, which are specified by thecondition specification unit 110, in an environment managed by the airconditioning control system 1. The learning model storage unit 300 canbe mounted on the environment management unit 100, a cell computer, ahost computer, a cloud server, a database server, or the like, forexample.

The learning model storage unit 300 stores a plurality of learningmodels 1, 2, . . . , N which are associated with combinations ofconditions (installation states and operation states of machines in afactory, for example), which are specified by the conditionspecification unit 110, in an environment managed by the airconditioning control system 1. The combinations of conditions(installation states and operation states of machines in a factory, forexample) in an environment managed by the air conditioning controlsystem 1 here represent combinations of values which can be taken byeach condition, ranges of values, and lists of values. In the case wherean environment managed by the air conditioning control system 1 ismodeled as illustrated in FIG. 2, for example, a matrix including statesof respective areas as elements [nothing, machine (high temperature),nothing, nothing, air conditioner, nothing, machine (mediumtemperature), nothing, nothing, nothing, machine (medium temperature),machine (stop)] may be used as one of combinations of conditions in anenvironment managed by the air conditioning control system 1.

Learning models stored in the learning model storage unit 300 are storedas information which can constitute one learning model complying withinference processing in the inference calculation unit 220. Leaningmodels stored in the learning model storage unit 300 may be stored asthe number of neurons (perception) of each layer or a weight parameteramong neurons (perceptron) of each layer, for example, in the case oflearning models using a learning algorithm of the multilayer neuralnetwork. Further, learning models stored in the learning model storageunit 300 may be stored as transition probability between a node and anode constituting the Bayesian network, for example, in the case oflearning models using a learning algorithm of the Bayesian network. Thelearning models stored in the learning model storage unit 300 may eachbe learning models using the same learning algorithm or may each belearning models using different learning algorithms. Thus, each of thelearning models stored in the learning model storage unit 300 may be alearning model using any learning algorithm as long as the learningmodels are applicable to inference processing by the inferencecalculation unit 220.

The learning model storage unit 300 may store one learning model in amanner such that it is associated with one combination of conditions inan environment managed by a single air conditioning control system 1 ormay store two or more learning models using different learningalgorithms in a manner such that they are associated with onecombination of conditions in an environment managed by a single airconditioning control system 1. The learning model storage unit 300 maystore learning models using different learning algorithms in a mannersuch that they are each associated with a plurality of combinations,ranges of which are overlapped with each other, of conditions in anenvironment managed by the air conditioning control system 1. In thiscase, the learning model storage unit 300 further sets use conditionssuch as required throughput and kinds of learning algorithms withrespect to learning models corresponding to combinations of conditionsin an environment managed by the air conditioning control system 1.Accordingly, it becomes possible to select learning models correspondingto the inference calculation units 220, whose executable inferenceprocessing and throughput are different, with respect to combinations ofconditions in an environment managed by the air conditioning controlsystem 1, for example.

When the learning model storage unit 300 receives a reading/writingrequest of a learning model including a combination of conditions in anenvironment managed by the air conditioning control system 1 from theoutside, the learning model storage unit 300 performs reading/writingwith respect to a learning model stored in a manner to be associatedwith the combination of conditions in the environment managed by the airconditioning control system 1. At this time, the learning modelreading/writing request may include information on inference processingwhich can be executed by the inference calculation unit 220 andthroughput of the inference calculation unit 220. In such case, thelearning model storage unit 300 performs reading/writing with inspect toa learning model which is associated with a combination of conditions inthe environment managed by the air conditioning control system 1,inference processing which can be executed by the inference calculationunits 220, and throughput of the inference calculation units 220. Thelearning model storage unit 300 may have a function of performingreading/writing with respect to a learning model which is associatedwith (a combination of) conditions, which are specified by the conditionspecification unit 110, based on these conditions, in response to areading/writing request of a learning model from the outside. Provisionof such function eliminates necessity of providing a function ofrequiring a learning model based on conditions, which are specified bythe condition specification unit 110, with respect to the inferencecalculation unit 220 and the learning model generation unit 500.

Here, the learning model storage unit 300 may encrypt a learning modelgenerated by the learning model generation unit 500 and store theencrypted learning model, and may decrypt the encrypted learning modelwhen the learning model is read by the inference calculation unit 220.

The air conditioning control unit 400 controls an operation of the airconditioner 130 which is installed in an environment managed by the airconditioning control system 1, based on a method for controlling the airconditioner 130, which is inferred by the inference processing unit 200,in the environment managed by the air conditioning control system 1.This air conditioning control unit 400 transmits a control command toeach air conditioner 130 via the network 150, for example, so as tocontrol each air conditioner 130. Further, this air conditioning controlunit 400 may be configured to control each air conditioner 130 via acommunication path (infrared rays and other wireless means, for example)different from the network 150.

The learning model generation unit 500 generates or updates a learningmodel stored in the learning model storage unit 300 (machine learning),based on conditions, which are specified by the condition specificationunit 110, in an environment managed by the air conditioning controlsystem 1 and a feature amount which is created by the feature amountcreation unit 210 and represents a feature of the environment managed bythe air conditioning control system 1. This learning model generationunit 500 selects a learning model which is to be an object of generationor updating based on conditions, which are specified by the conditionspecification unit 110, in an environment managed by the airconditioning control system 1 and performs machine learning with respectto the selected learning model, based on a feature amount which iscreated by the feature amount creation unit 210 and represents a featureof the environment managed by the air conditioning control system 1.Examples of timing at which the learning model generation unit 500performs learning include a timing at which an operator manually changessetting of each air conditioner 130. In this case, the learning modelgeneration unit 500 performs generation or updating (machine learning)of a learning model by using a feature amount, which is created by thefeature amount creation unit 210 and represents a feature of theenvironment managed by the air conditioning control system 1, as a statevariable and using a set temperature and a wind direction, for example,of each air conditioner 130 as label data, with respect to a learningmodel selected based on conditions in the environment managed by the airconditioning control system 1.

In the case where a learning model associated with (a combination of)conditions, which are specified by the condition specification unit 110,in an environment managed by the air conditioning control system 1 isnot stored in the learning model storage unit 300, the learning modelgeneration unit 500 newly generates a learning model which is associatedwith (the combination) of the conditions. On the other hand, in the casewhere a learning model associated with (a combination of) conditions,which are specified by the condition specification unit 110, in anenvironment managed by the air conditioning control system 1 is storedin the learning model storage unit 300, the learning model generationunit 500 performs machine learning with respect to this learning modelso as to update this learning model. In the case where a plurality oflearning models associated with (combinations) of conditions, which arespecified by the condition specification unit 110, in an environmentmanaged by the air conditioning control system 1 are stored in thelearning model storage unit 300, the learning model generation unit 500may perform machine learning with respect to each of the learning modelsor may perform machine learning with respect to only a part of thelearning models based on learning processing which can be executed bythe learning model generation unit 500 and throughput of the learningmodel generation unit 500.

The learning model generation unit 500 may alter a learning model storedin the learning model storage unit 300 and generate a new learningmodel. As an example of the alteration of a learning model by thelearning model generation unit 500, generation of a distilled model isexemplified. A distilled model is a learned model which is obtained suchthat learning is performed from the beginning in a machine learningdevice by using an output obtained with respect to an input into anothermachine learning device in which a learned model is incorporated. Thelearning model generation unit 500 can store a distilled model, which isobtained through such processing (called distillation processing), inthe learning model storage unit 300 as a new learning model and can usethe new learning model. A distilled model is generally smaller in sizethan an original learned model but exhibits the same accuracy as that ofthe original learned model, being more suitable for distribution toother computers through a network or the like. Another example of thealteration of a learning model by the learning model generation unit 500is integration of learning models. In the case where structures of twoor more learning models which are stored in a manner to be associatedwith (combinations of) conditions in an environment managed by the airconditioning control system 1 are similar to each other, for example, inthe case where values of respective weight parameters are within apredetermined threshold value, the learning model generation unit 500may integrate (the combinations of) conditions, which are associatedwith these learning models, in the environment managed by the airconditioning control system 1 and may store any of the two or morelearning models, whose structures are similar to each other, in a mannerto associate any of the two or more learning models with the integratedcondition.

FIG. 3 is a functional block diagram schematically illustrating an airconditioning control system 1 according to a second embodiment.

In the air conditioning control system 1 according to the presentembodiment, each functional block is mounted on one air conditioningcontroller 2 (which is to be structured on a central management deviceof air conditioners, a numerical controller, or the like). In suchconfiguration, the air conditioning control system 1 according to thepresent embodiment infers a method for controlling the air conditioners130 in an environment managed by the air conditioning control system 1by using a learning model differing depending on installation states andoperation states of the machines 120 in the environment managed by theair conditioning control system 1 so as to control the air conditioners130 in the environment managed by the air conditioning control system 1.Further, one air conditioning controller 2 is capable ofgenerating/updating learning models each corresponding to conditions inan environment managed by the air conditioning control system 1.

FIG. 4 is a functional block diagram schematically illustrating an airconditioning control system 1 according to a third embodiment.

In the air conditioning control system 1 according to the presentembodiment, the environment management unit 100, the inferenceprocessing unit 200, and the air conditioning control unit 400 aremounted on the air conditioning controller 2, and the learning modelstorage unit 300 and the learning model generation unit 500 are mountedon a machine learning device 3 which is connected with the airconditioning controller 2 via standard interface and network. Themachine learning device 3 may be mounted on a cell computer, a hostcomputer, a cloud server, or a database server. In such configuration,inference processing using a learned model, which is relatively lightprocessing, can be executed on the air conditioning controller 2 andprocessing for generating/updating a learning model, which is relativelyheavy processing, can be executed on the machine learning device 3, sothat the air conditioning control system 1 can be operated withoutinterrupting an intrinsic action of the air conditioning controller 2.

FIG. 5 is a functional block diagram schematically illustrating an airconditioning control system 1 according to a fourth embodiment.

In the air conditioning control system 1 according to the presentembodiment, the environment management unit 100 is mounted on the airconditioning controller 2, and the inference calculation unit 220, thelearning model storage unit 300, and the learning model generation unit500 are mounted on the machine learning device 3 which is connected withthe air conditioning controller 2 via standard interface and network.Further, the air conditioning control unit 400 is separately prepared.In the air conditioning control system 1 according to the presentembodiment, the configuration of the feature amount creation unit 210 isomitted on the assumption that a state amount detected by the stateamount detection unit 140 is data which can be directly used forinference processing by the inference calculation unit 220 andgeneration/updating processing of a learning model performed by thelearning model generation unit 500. In such configuration, inferenceprocessing using a learned model and generation/updating processing of alearning model can be executed on the machine learning device 3, so thatthe air conditioning control system 1 can be operated withoutinterrupting an intrinsic action of the air conditioning controller 2.

FIG. 6 is a functional block diagram schematically illustrating an airconditioning control system 1 according to a fifth embodiment.

In the air conditioning control system 1 according to the presentembodiment, each functional block is mounted on one air conditioningcontroller 2. In the air conditioning control system 1 according to thepresent embodiment, the configuration of the learning model generationunit 500 is omitted on the assumption that a plurality of learningmodels that have been learned and are associated with combinations ofconditions in an environment managed by the air conditioning controlsystem 1 are already stored in the learning model storage unit 300 andgeneration/updating of a learning model is not performed. In suchconfiguration, the air conditioning control system 1 according to thepresent embodiment infers a method for controlling the air conditioners130 in an environment managed by the air conditioning control system 1by using a learning model differing depending on installation states andoperation states of the machines 120 in the environment managed by theair conditioning control system 1, for example, so as to control the airconditioners 130 in the environment managed by the air conditioningcontrol system 1. Further, since a learning model is not arbitrarilyupdated, this configuration is employable as the configuration of theair conditioning controller 2 which is shipped to customers, forexample.

FIG. 7 is a functional block diagram schematically illustrating amodification of the air conditioning control system 1 according to thefifth embodiment.

The air conditioning control system 1 according to the presentmodification is an example in which the learning model storage unit 300is mounted on an external storage 4 which is connected with the airconditioning controller 2 in the fifth embodiment (FIG. 6). In thepresent modification, learning models having larger capacity are storedin the external storage 4, so that more learning models can be used andlearning models can be read without any intervention of a network or thelike. Thus, this modification is beneficial when a real time property isrequired for inference processing.

FIG. 8 is a functional block diagram schematically illustrating an airconditioning control system 1 according to a sixth embodiment.

In the air conditioning control system 1 according to the presentembodiment, the environment management unit 100 is mounted on the airconditioning controller 2, and the inference calculation unit 220 andthe learning model storage unit 300 are mounted on the machine learningdevice 3 which is connected with the air conditioning controller 2 viastandard interface and network. The machine learning device 3 may bemounted on a cell computer, a host computer, a cloud server, or adatabase server. In the air conditioning control system 1 according tothe present embodiment, the configuration of the learning modelgeneration unit 500 is omitted on the assumption that a plurality oflearning models that have been learned and are associated withcombinations of conditions in an environment managed by the airconditioning control system 1 are already stored in the learning modelstorage unit 300 and generation/updating of a learning model is notperformed. In the air conditioning control system 1 according to thepresent embodiment, the configuration of the feature amount creationunit 210 is omitted on the assumption that a state amount detected bythe state amount detection unit 140 is data which can be directly usedfor inference processing by the inference calculation unit 220. In suchconfiguration, the air conditioning control system 1 according to thepresent embodiment infers a method for controlling the air conditioners130 in an environment managed by the air conditioning control system 1by using a learning model differing depending on installation states andoperation states of the machines 120 in the environment managed by theair conditioning control system 1, for example, so as to control the airconditioners 130 in the environment managed by the air conditioningcontrol system 1. Further, since a learning model is not arbitrarilyupdated, this configuration is employable as the configuration of theair conditioning controller 2 which is shipped to customers, forexample.

FIG. 9 is a schematic flowchart of processing executed in the airconditioning control system 1 according to the present invention.

The flowchart illustrated in FIG. 9 shows an example of a flow ofprocessing in the case where updating of a learning model is notperformed in the air conditioning control system 1 (the fifth and sixthembodiments).

[Step SA01] The condition specification unit 110 specifies conditions inan environment managed by the air conditioning control system 1.

[Step SA02] The state amount detection unit 140 detects a state of theenvironment managed by the air conditioning control system 1 as a stateamount.

[Step SA03] The feature amount creation unit 210 creates a featureamount representing a feature of the environment managed by the airconditioning control system 1 based on the state amount detected in stepSA02.

[Step SA04] The inference calculation unit 220 selects and reads alearning model corresponding to the conditions, which are specified instep SA01, in the environment managed by the air conditioning controlsystem 1 from the learning model storage unit 300 as a learning model tobe used for inference.

[Step SA05] The inference calculation unit 220 infers a method forcontrolling the air conditioners 130 in the environment managed by theair conditioning control system 1 based on the learning model read instep SA04 and the feature amount created in step SA03.

[Step SA06] The air conditioning control unit 400 controls airconditioning based on the method for controlling air conditioning, whichis inferred in step SA05.

FIG. 10 is a schematic flowchart of processing executed in the airconditioning control system 1 according to the present invention.

The flowchart illustrated in FIG. 10 shows an example of ies a flow ofprocessing in the case where generation/updating of a learning model isperformed in the air conditioning control system 1 (the first to fourthembodiments).

[Step SB01] The condition specification unit 110 specifies conditions inan environment managed by the air conditioning control system 1.

[Step SB02] The state amount detection unit 140 detects a state of theenvironment managed by the air conditioning control system 1 as a stateamount.

[Step SB03] The feature amount creation unit 210 creates a featureamount representing a feature of the environment managed by the airconditioning control system 1 based on the state amount detected in stepSB02.

[Step SB04] The inference calculation unit 220 selects and reads alearning model corresponding to the conditions, which are specified instep SB01, in the environment managed by the air conditioning controlsystem 1 from the learning model storage unit 300 as a learning model tobe used for inference.

[Step SB05] The learning model generation unit 500 determines whether ornot a learning model that has been learned and corresponds to theconditions, which are specified in step SB01, in the environment managedby the air conditioning control system 1 is already generated in thelearning model storage unit 300. If a learning model that has beenlearned is already generated, the processing proceeds to step SB07. If alearning model that has been learned is not generated yet, theprocessing proceeds to step SB06.

[Step SB06] The learning model generation unit 500 generates/updates alearning model corresponding to the conditions, which are specified instep SB01, in the environment managed by the air conditioning controlsystem 1 based on the feature amount created in step SB03 and theprocessing proceeds to step SB01.

[Step SB07] The inference calculation unit 220 infers a method forcontrolling the air conditioners 130 in the environment managed by theair conditioning control system 1 based on the learning model read instep SB04 and the feature amount created in step SB03.

[Step SB08] The air conditioning control unit 400 controls airconditioning based on the method for controlling air conditioning, whichis inferred in step SB05.

The embodiments of the present invention have been described above. Thepresent invention is not limited to the examples of the above-describedembodiments but may be embodied in various aspects by adding arbitraryalterations.

1. An air conditioning control system that controls an air conditionerin an environment in which at least one machine is installed, the airconditioning control system comprising: a condition specification unitthat specifies a condition in the environment; a state amount detectionunit that detects a state amount representing a state of theenvironment; an inference calculation unit that infers a control methodfor the air conditioner in the environment based on the state amount; anair conditioning control unit that controls the air conditioner based onthe control method that is inferred by the inference calculation unit; alearning model generation unit that generates or updates a learningmodel through machine learning using the state amount; and a learningmodel storage unit that stores one or more learning models generated bythe learning model generation unit in a manner such that the one or morelearning model are associated with a combination of conditions specifiedby the condition specification unit, wherein the inference calculationunit calculates a control method for the air conditioner in theenvironment managed by the air conditioning control system, byselectively using one or more learning models among learning modelsstored in the learning model storage unit, based on the condition in theenvironment that is specified by the condition specification unit. 2.The air conditioning control system according to claim 1, furthercomprising: a feature amount creation unit that creates a feature amountcharacterizing the environment based on the state amount detected by thestate amount detection unit, wherein the inference calculation unitinfers a control method for the air conditioner in the environment basedon the feature amount, and the learning model generation unit generatesor updates learning model through machine learning using the featureamount.
 3. The air conditioning control system according to claim 1,wherein the learning model generation unit alters an existing learningmodel stored in the learning model storage unit so as to generate a newlearning model.
 4. The air conditioning control system according toclaim 1, wherein the learning model storage unit encrypts and stores alearning model generated by the learning model generation unit, anddecrypts the encrypted learning model when the learning model encryptedis read by the inference calculation unit.
 5. An air conditioningcontrol system that controls an air conditioner in an environment inwhich one or more machines are installed the air conditioning controlsystem comprising: a condition specification unit that specifies acondition in the environment; a state amount detection unit that detectsa state amount representing the environment; an inference calculationunit that infers a control method for the air conditioner in theenvironment based on the state amount; an air conditioning control unitthat controls the air conditioner based on the control method that isinferred by the inference calculation unit; and a learning model storageunit that stores at least one learning model that is preliminarilyassociated with a combination of conditions in the environment, whereinthe inference calculation unit calculates a control method for the airconditioner in the environment by selectively using one or more learningmodels among the learning models stored in the learning model storageunit, based on the condition in the environment that is specified by thecondition specification unit
 6. The air conditioning control systemaccording to claim 5, further comprising: a feature amount creation unitthat creates a feature amount characterizing the environment based onthe state amount, wherein the inference calculation unit infers, basedon the feature amount, a control method for the air conditioner in theenvironment managed by the air conditioning control system.
 7. An airconditioning controller comprising: the condition specification unitaccording to claim 1; and the state amount detection unit according toclaim
 1. 8. An air conditioning controller comprising: the conditionspecification unit according to claim 5; and the state amount detectionunit according to claim 5
 9. An air conditioning control methodcomprising: a step for specifying a condition for controlling an airconditioner in an environment in which one or more machines areinstalled; a step for detecting a state amount representing theenvironment; a step for inferring a control method for the airconditioner in the environment based on the state amount; a step forcontrolling the air conditioner based on the control method; and a stepfor generating or updating a learning model through machine learningusing the state amount, wherein in the step for inferring, a learningmodel to be used based on the condition in the environment that isspecified in the step for specifying is selected from the one or morelearning models that are preliminarily associated with a combination ofconditions in the environment, and a control method for the airconditioner in the environment is calculated using the learning modelthat is selected.
 10. The air conditioning control method according toclaim 9, further comprising: a step for creating a feature amountcharacterizing the environment based on the state amount, wherein in thestep for inferring, a control method for the air conditioner in theenvironment is inferred based on the feature amount, and in the step forgenerating or updating a learning model, a learning model is generatedor updated through machine learning using the feature amount.
 11. An airconditioning control method comprising: a step for specifying acondition for controlling an air conditioner in an environment in whichone or more machines are installed; a step for detecting a state amountrepresenting the environment; a step for inferring a control method forthe air conditioner in the environment based on the state amount; and astep for controlling the conditioner based on the control method,wherein in the step for inferring, a learning model to be used based onthe condition in the environment that is specified in the step forspecifying is selected from one or more learning models that arepreliminarily associated with a combination of conditions in theenvironment, and a control method for the air conditioner in theenvironment is calculated using the learning model that is selected. 12.The air conditioning control method according to claim 11, furthercomprising: a step for creating a feature amount characterizing theenvironment based on the state amount, wherein in the step forinferring, a control method for the air conditioner in the environmentis inferred based on the feature amount.
 13. A learning model setcomprising: a plurality of learning models each of which is associatedwith a combination of conditions for controlling an air conditioner inan environment in which one or more machines are installed, wherein eachof the plurality of learning models is a learning model generated orupdated, in a condition in the environment, based on a state amountrepresenting the environment, and one learning model is selected fromthe plurality of learning models based on a condition set in anenvironment, and the learning model that is selected is used forprocessing of inferring a control method for the air conditioner in theenvironment.